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https://github.com/SVA-SE/HSO

Health Surveillance Ontology
https://github.com/SVA-SE/HSO

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Health Surveillance Ontology

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README 

        
# (One-) Health Surveillance Ontology (HSO)



The development of a framework of One Health Surveillance (OHS) faces varied data interoperability challenges - among institutions, across health surveillance sectors, and among countries. Interoperability is used here to mean “the ability of different information technology systems and software applications to communicate, exchange data, and use the information that has been exchanged”. *Semantic interoperability*, in particular, is concerned with ensuring the integrity and meaning of the data across systems. Semantic interoperability is particularly important in One Health in order to allow data reuse across sectors, and even reuse of data for research and knowledge discovery. 

 

HSO aims to support data interoperability in One-health Surveillance. “An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them”. [(Noy & Mcguiness, 2001)](http://protege.stanford.edu/publications/ontology\_development/ontology101.pdf)



Several terminology catalogues already exist in health and epidemiology, and we highlight in particular those implemented by EFSA and ECDC to achieve structural interoperability among member states (MS). Ontologies can incorporate these existing resources and re-use all their knowledge. But we move beyond the listing of concepts and include also “relationships” between concepts (semantics), creating a knowledge model for health surveillance. A machine-interpretable version of the domain knowledge offers several advantages, in particular: 

•	Use of automated reasoners to make inferences and detect errors in the data.

•	Flexibility to accommodate to knowledge growth and updates. 

•	Reuse. Ontologies are meant to model specific pieces of knowledge, in a way that allows linking to complementary pieces. As epidemiology is highly multi-disciplinary, the use of ontologies allows us to piece together expertise from many different domains. 

•	Interoperability. Terminologies allow humans to understand each other and agree on what things mean. Ontologies allow software to talk to each other.



You can find background materials on ontologies and their use here: [http://datadrivensurveillance.org/ontology/](http://datadrivensurveillance.org/ontology/).

 



## Communication and Involvement

* Contact us writing to **[email protected]_**. 



## Project management and structure

This project is managed by the [Swedish National Veterinary Institute](http://www.sva.se). 

This project started as the Animal health Surveillance Ontology, funded by the Swedish Innovation Agency - [Vinnova](http://www.vinnova.se/en/).

Its continued development into HSO is supported by the [ORION project](https://onehealthejp.eu/structure/jip1-orion/), funded by the [One-Health European Joint Programme](https://onehealthejp.eu), which has received funding from the European Union’s Horizon 2020

research and innovation programme under Grant Agreement No 773830.



### Current core members 

* Fernanda Dórea, project leader: National Veterinary Institute, Sweden

* Crawford Revie: University of Prince Edward Island, Canada

* Ann Lindberg: National Veterinary Institute, Sweden

* Eva Blomqvist: Linköping University, Sweden

* Patrick Lambrix: Linköping University, Sweden

* Karl Hammar: Jönköping University, Sweden



Help and advice from https://genepio.org/



**Note that HSO is under early stages of development.**